Detection of COVID-19 from speech signal using bio-inspired based cepstral features

Abstract

The early detection of COVID-19 is a challenging task due to its deadly spreading nature and existing fear in minds of people. Speech-based detection can be one of the safest tools for this purpose as the voice of the suspected can be easily recorded. The Mel Frequency Cepstral Coefficient (MFCC) analysis of speech signal is one of the oldest but potential analysis tools. The performance of this analysis mainly depends on the use of conversion between normal frequency scale to perceptual frequency scale and the frequency range of the filters used. Traditionally, in speech recognition, these values are fixed. But the characteristics of speech signals vary from disease to disease. In the case of detection of COVID-19, mainly the coughing sounds are used whose bandwidth and properties are quite different from the complete speech signal. By exploiting these properties the efficiency of the COVID-19 detection can be improved. To achieve this objective the frequency range and the conversion scale of frequencies have been suitably optimized. Further to enhance the accuracy of detection performance, speech enhancement has been carried out before extraction of features. By implementing these two concepts a new feature called COVID-19 Coefficient (C-19CC) is developed in this paper. Finally, the performance of these features has been compared.

Keywords: Bio-inspired computing; COVID19; Speech signal.

Fig. 1

Steps for the MFCC feature extraction.

Fig. 2

Steps for the Filter bank computation.

Fig. 3

Block Diagram of Proposed detection model of COVID-19.

Fig. 4

Flow Chart of Fire Fly Algorithm.

Fig. 5

Effect of change in the Delta value and maximum frequency on the Validation Accuracy.

Fig. 6

Flow Chart of Improved phase aware speech enhancement Scheme.

Fig. 7

Performance analysis of the TFBCC-M features.

Fig. 8

Comparison of the performance of standard MFCC features for databse-1.

Fig. 9

The learning characteristics obtained during the optimization of FF algorithm.

Fig. 10

Comparison of the validation accuracy and sensitivity using different feature sets.

Fig. 11

Comparison of the F-2 score and average p-value using different feature sets.